Anomalous Temperature Analysis System Design of Belt Conveyor Based on Snake Optimization (SO) Algorithm

Taking the belt conveyor as the research object, a system for analyzing the abnormal temperature of its key parts is built. The system analyzes the extent of the impact of faults on the operation of the belt conveyor and determines the key monitoring parts of the belt conveyor temperature. The system selects the appropriate temperature measurement scheme, designs the sensor layout scheme, and finally builds the system scheme for temperature monitoring of the critical parts of the belt conveyor. The temperature prediction model building method based on deep LSTM network is proposed. The time series characteristics of temperature data are analyzed, the principle of gating mechanism of LSTM neural network and the process of error reversal delay calculation are studied, and the prediction effect is optimized by using VMD-SO. The final model performance evaluation system is established, and the predicted values provide support for the system’s temperature warning and other functions.